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RISS 인기검색어
- 검색키워드
- 저자 : ( Anant Sharma ) (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Clinico-pathological Profile of Lung Cancer at AIIMS: A Changing Paradigm in India
Malik, Prabhat Singh,Sharma, Mehar Chand,Mohanti, Bidhu Kalyan,Shukla, N.K.,Deo, S.V.S.,Mohan, Anant,Kumar, Guresh,Raina, Vinod Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.1
Background: Lung cancer is one of the commonest and most lethal cancers throughout the world. The epidemiological and pathological profile varies among different ethnicities and geographical regions. At present adenocarcinoma is the commonest histological subtype of non-small cell lung cancer (NSCLC) in most of the Western and Asian countries. However, in India squamous cell carcinoma has been reported as the commonest histological type in most of the series. The aim of the study was to analyze the current clinico-pathological profile and survival of lung cancer at our centre. Materials and Methods: We analyzed 434 pathologically confirmed lung cancer cases registered at our centre over a period of three years. They were evaluated for their clinical and pathological profiles, treatment received and outcome. The available histology slides were reviewed by an independent reviewer. Results: Median age was 55 years with a male:female ratio of 4.6:1. Some 68% of patients were smokers. There were 85.3% NSCLC and 14.7% SCLC cases. Among NSCLCs, adenocarcinoma was the commonest histological subtype after the pathology review. Among NSCLC, 56.8% cases were of stage IV while among SCLC 71.8% cases had extensive stage disease. Some 29% of patients did not receive any anticancer treatment. The median overall and progression free survivals of the patients who received treatment were 12.8 and 7.8 months for NSCLC and 9.1 and 6.8 months for SCLC. Conclusions: This analysis suggests that adenocarcinoma may now be the commonest histological subtype also in India, provided a careful pathological review is done. Most of the patients present at advanced stage and outcome remains poor.
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The Single Cigarette Economy in India - a Back of the Envelope Survey to Estimate its Magnitude
Lal, Pranay,Kumar, Ravinder,Ray, Shreelekha,Sharma, Narinder,Bhattarcharya, Bhaktimay,Mishra, Deepak,Sinha, Mukesh K.,Christian, Anant,Rathinam, Arul,Singh, Gurbinder Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.13
Background: Sale of single cigarettes is an important factor for early experimentation, initiation and persistence of tobacco use and a vital factor in the smoking epidemic in India as it is globally. Single cigarettes also promote the sale of illicit cigarettes and neutralises the effect of pack warnings and effective taxation, making tobacco more accessible and affordable to minors. This is the first study to our knowledge which estimates the size of the single stick market in India. Materials and Methods: In February 2014, a 10 jurisdiction survey was conducted across India to estimate the sale of cigarettes in packs and sticks, by brands and price over a full business day. Results: We estimate that nearly 75% of all cigarettes are sold as single sticks annually, which translates to nearly half a billion US dollars or 30 percent of the India's excise revenues from all cigarettes. This is the price which the consumers pay but is not captured through tax and therefore pervades into an informal economy. Conclusions: Tracking the retail price of single cigarettes is an efficient way to determine the willingness to pay by cigarette smokers and is a possible method to determine the tax rates in the absence of any other rationale.
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Srinivasan, Kathiravan,Chang, Chuan-Yu,Huang, Chao-Hsi,Chang, Min-Hao,Sharma, Anant,Ankur, Avinash Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.4
Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.
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( Kathiravan Srinivasan ),( Chuan-yu Chang ),( Chao-hsi Huang ),( Min-hao Chang ),( Anant Sharma ),( Avinash Ankur ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.4
Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.
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